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1st International Conference on Electronic and Electrical Engineering and Intelligent System (ICE3IS)

作者： Yunus, Nurul Md Affande, Noor Aliff Noor Ramli, Roshahliza M. Noor, Ali O. Abid Samad, Salina Abdul Univ Malaysia Pahang Fac Elect & Elect Engn Technol Pekan Malaysia Univ Technol Iraq Dept Commun Engn Baghdad Iraq Univ Kebangsaan Malaysia Fac Engn & Built Environm Dept Elect Elect & Syst Engn Bangi Selangor Malaysia

ISBN: (纸本)9781665405461

This research aims to investigate the preprocessing stage of the noise cancellation system to tackle the problem of hearing loss in the industry. A hearing aid may help one to hear sounds, but it will not filter or eliminate background sound. Hence, it is hard to hear clearly. Other than that, for a worker who is working in a high noise environment, repeated exposure to loud noise can lead to severe hearing degradation or permanent hearing loss. There are four proposed filters used for this paper, and four proposed adaptive algorithms in the Assistive Listening System (ALS). These filters and adaptive algorithms are significant to investigate the preprocessing to achieve a high level of Signal-to-Noise Ratio (SNR) performance with a low noise level. Preprocessing method is crucial to minimize the background noises and to eliminate the risk of corrupt hearing. After the findings, the best filter used for preprocessing is the Butterworth Lowpass Filter with a maximum output power of 65 dB;and the best adaptive algorithm is Smart Noise Canceller with an output SNR of 80.43 dB.

关键词： Signal-to-Noise Ratio Noise Reduction Preprocessing adaptive filter adaptive algorithm

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A Hybrid Model and Learning-Based adaptive Navigation Filter

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2022年 71卷

作者： Or, Barak Klein, Itzik Univ Haifa Charney Sch Marine Sci Dept Marine Technol IL-3498838 Haifa Israel

The fusion between an inertial navigation system and global navigation satellite systems is regularly used in many platforms, such as drones, land vehicles, and marine vessels. The fusion is commonly carried out in a model-based extended Kalman filter framework. One of the critical parameters of the filter is the process noise covariance. It is responsible for the real-time solution accuracy, as it considers both vehicle dynamics uncertainty and the inertial sensors quality. In most situations, the process noise covariance is assumed to be constant. Yet, due to vehicle dynamics and sensor measurement variations throughout the trajectory, the process noise covariance is subject to change. To cope with such situations, several adaptive model-based Kalman filters were suggested in the literature. In this article, we propose a hybrid model and learning-based adaptive navigation filter. We rely on the model-based Kalman filter and design a deep neural network (DNN) model to tune the momentary system noise covariance, based only on the inertial sensor readings. Once the process noise covariance is learned, it is plugged into the well-established model-based Kalman filter. After deriving the proposed hybrid framework, field experiment results using a quadrotor are presented and a comparison to model-based adaptive approaches is given. We show that the proposed method obtained an improvement of 25% in the position error. Furthermore, the proposed hybrid learning method can be used in any navigation filter and also in any relevant estimation problem.

关键词： Navigation Adaptation models Global navigation satellite system Kalman filters Vehicle dynamics Noise measurement Inertial navigation adaptive algorithm deep neural network (DNN) global navigation satellite system (GNSS) inertial measurement unit (IMU) inertial navigation system (INS) Kalman Filter machine learning (ML) quadcopter supervised learning (SL) unmanned autonomous vehicles vehicle tracking

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A novel method to reduce indoor signal power fluctuation using multiple adaptive terahertz sources

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2022年第11期35卷 e5196-e5196页

作者： Liang, Yewen Jing, Qingfeng Zhong, Weizhi Nanjing Univ Aeronaut & Astronaut Coll Astronaut Nanjing 210000 Peoples R China

The indoor terahertz sources cause power fluctuation at various locations due to obstacles, which can be avoided by deploying multiple sources, but will lead to high energy consumption. In this paper, the reverse ray-tracing method is used to establish a terahertz indoor channel. The free path loss, atmospheric absorption, and reflection loss are considered. adaptive algorithms are adopted to solve the large energy consumption of deploying multiple sources. A weight vector coefficient is used based on the location and received user signal to adjust the transmission power of each terahertz source dynamically. The results show that the adaptive power control of multiple terahertz sources substantially reduces the energy consumption and the signal power fluctuations. For static users, the energy consumption can be reduced by 5 and 9 dB, and the maximum data transmission rate can be increased by one to two times using four and nine sources, respectively. Moreover, energy consumption can be reduced by more than 6 dB for several indoor users. For dynamic users with a changing channel environment, the adaptive algorithm can adjust the source power in real time and improve signal efficiency. However, the efficiency improvement of the algorithm is limited by the distribution of the multiple sources. The energy consumption of four sources with the adaptive algorithm operating in the center of the room is greater than that of nine sources without the adaptive algorithm. Nevertheless, the efficiency of the nine sources with the adaptive algorithm is much higher than that of the four sources.

关键词： adaptive algorithm indoor communication multiple sources terahertz

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Cognitive Active Sonar Tracking for Optimum Performance in Clutter 24

Cognitive Active Sonar Tracking for Optimum Performance in C...

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24th IEEE International Conference on Information Fusion (FUSION)

作者： Grimmett, Doug Abraham, Doug Alberto, Ricki NIWC Pacific San Diego CA 92152 USA CausaSci LLC Ellicott City MD USA

ISBN: (纸本)9781737749714

In this paper, a "cognitive" active sonar tracking algorithm is described, and results of its application to data from the LCAS'15 sea trial are shown. A key factor in tracker performance is the scheme used for track initiation and termination. A very common track initiation algorithm (TIA) is the sliding M-of-N processor, however, the tuning of its parameters can be difficult. It is often heuristic and sub-optimum, in achieving both good tracking performance of true targets as well as controlling the false track rate (FTR) for a desired sonar P-d/P-f alpha operating point. This is of particular concern for clutter-rich reverberation-limited undersea acoustic environments, where the false-alarm rates are high. The algorithm utilizes available in-situ data to estimate the statistics of the encountered clutter, and then optimizes tracker performance to meet specified operational levels. The adaptive algorithm is shown to effectively control the false track rate. The algorithm has potential to cognitively self-tune its operations for optimum performance.

关键词： sonar tracking adaptive algorithm clutter track initiation false track rate M-of-N

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MAB-OS: Multi-Armed Bandits Metaheuristic Optimizer Selection

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APPLIED SOFT COMPUTING 2022年第0期128卷

作者： Meidani, Kazem Mirjalili, Seyedali Farimani, Amir Barati Carnegie Mellon Univ Dept Mech Engn Pittsburgh PA 15213 USA Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat Adelaide SA Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea Carnegie Mellon Univ Machine Learning Dept Pittsburgh PA USA Carnegie Mellon Univ Dept Biomed Engn Pittsburgh PA USA

Metaheuristic algorithms are derivative-free optimizers designed to estimate the global optima for optimization problems. Keeping balance between exploitation and exploration and the performance complementarity between the algorithms have led to the introduction of quite a few metaheuristic methods. In this work, we propose a framework based on Multi-Armed Bandits (MAB) problem, which is a classical Reinforcement Learning (RL) method, to intelligently select a suitable optimizer for each optimization problem during the optimization process. This online algorithm selection technique leverages on the convergence behavior of the algorithms to find the right balance of explorationexploitation by choosing the update rule of the algorithm with the most estimated improvement in the solution. By performing experiments with three armed-bandits being Harris Hawks Optimizer (HHO), Differential Evolution (DE), and Whale Optimization algorithm (WOA), we show that the MAB Optimizer Selection (named as MAB-OS) framework has the best overall performance on different types of fitness landscapes in terms of both convergence rate and the final solution. The data and codes used for this work are available at: https://***/BaratiLab/MAB-OS. (c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by- nc- nd/4.0/).

关键词： Metaheuristic Optimization Multi-Armed Bandits Reinforcement Learning algorithm selection adaptive algorithm

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A recovery-based a posteriori error estimator of the weak Galerkin finite element method for elliptic problems

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JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2022年第0期406卷 113926-113926页

作者： Liu, Ying Wang, Gang Wu, Mengyao Nie, Yufeng Northwestern Polytech Univ Res Ctr Computat Sci Sch Math & Stat Xi'an 710129 Shaanxi Peoples R China

In this paper, we propose a recovery-type a posteriori error estimator of the weak Galerkin finite element method for the second order elliptic equation. The reliability and efficiency of the estimator are analyzed by a discrete H-1-norm of the exact error. The estimator is further used in the adaptive weak Galerkin algorithm on the triangular, quadrilateral and other polygonal meshes. Numerical results are provided to demonstrate the effectiveness of the adaptive mesh refinement guided by this estimator. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Weak Galerkin finite element method Weak gradient recovery A posteriori error estimator adaptive algorithm

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Online metaheuristic algorithm selection

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期201卷 1页

作者： Meidani, Kazem Mirjalili, Seyedali Farimani, Amir Barati Carnegie Mellon Univ Dept Mech Engn Pittsburgh PA 15232 USA Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat Adelaide SA Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea Carnegie Mellon Univ Machine Learning Dept Pittsburgh PA 15213 USA Carnegie Mellon Univ Dept Biomed Engn Pittsburgh PA 15213 USA

The performance of optimization algorithms significantly depends on the landscape of the problems. It is known that there is no single algorithm that outperforms others on problems with different fitness landscapes. One of the issues in metaheuristic algorithms is keeping the balance between exploration and exploitation. The features extracted from analysis of fitness landscapes can be used to select the suitable algorithm for the given problem. However, these features are usually expensive and extracted prior to the optimization process which leads to a single algorithm to be selected. In this work, we propose an intelligent switch mechanism that enjoys an efficient non-convex ratio (ENCR) feature extracted online during the optimization to switch between two choices of algorithms, each favoring a type of landscape in terms of modality. For this work, two case studies including a pair of Harris hawks optimizer (HHO) and differential evolution (DE) and another pair of multiverse optimizer (MVO) and moth-flame optimizer (MFO) are selected among several algorithms to evaluate the performance of this framework. The proposed one-way and two-way switch algorithms take advantage of the merits of the two base algorithms to reach better final solutions and higher convergence rates in the majority of case studies. The overall comparison and ranking of the algorithms, including a random switch baseline, demonstrates the superiority of the intelligent switch mechanisms over the baselines.

关键词： Metaheuristic optimization Fitness landscape analysis Efficient non-convex ratio algorithm selection Intelligent switch mechanism adaptive algorithm Optimization algorithm Benchmark

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Integration of BWT scrambling and data compression in an innovative system enhances protection and versatile management of sensor feeds (SEC)

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Heliyon 2024年第20期10卷 e39254页

作者： Begum, M. Baritha Kaliyaperumal, Karthikeyan Department of Electronics and Communication Engineering Saranathan College of Engineering Tamil Nadu Tiruchirapalli 620012 India IT@IoT- HH Campus Ambo University Ethiopia

The proposed Sensor Data Encryption with Compression (SEC) system is presented as an innovative solution for sensor data processing, aiming to achieve optimal efficiency while improving security and adaptability by integrating robust cryptography with advanced compression techniques. The system's objective is to create a reliable framework for managing sensitive sensor data by effectively tackling challenges in data processing through a balanced combination of strong cryptography and sophisticated compression methods. The SEC system utilizes advanced encryption and compression techniques, including the scrambling of Burrows-Wheeler Transform (BWT), Move-to-Front (MTF) encoding, Run-Length Encoding (RLE), and Huffman coding, to attain exceptional compression efficiency while maintaining data integrity. The practical implementation of the SEC system shows an impressive 85 % enhancement in data compression efficiency, as evidenced by reduced Bits per Character (BPC) across various sensor data inputs, along with improved resistance to cryptanalysis due to an increased unicity distance. Consequently, the SEC system demonstrates itself to be a robust and efficient solution, achieving superior data compression and enhanced cryptographic resilience, thereby addressing critical challenges in sensor data processing. © 2024

关键词： adaptive algorithm Compression efficiency Cryptographic resilience Information security Sensor data processing Unicity distance

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An adaptive enriched semi-Lagrangian finite element method for coupled flow-transport problems

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COMPUTERS & FLUIDS 2022年 240卷 1页

作者： Ouardghi, Abdelouahed El-Amrani, Mofdi Seaid, Mohammed Univ Mohammed VI Polytech Int Water Res Inst Benguerir Morocco Univ Rey Juan Carlos Dept Matemat Applicada Ciencia Ingn Mat & Tecnol Elect Mostoles 28933 Madrid Spain Univ Durham Dept Engn South Rd Durham DH1 3LE England

An adaptive enriched semi-Lagrangian finite element method is proposed for the numerical solution of coupled flow-transport problems on unstructured triangular meshes. The new method combines the semi-Lagrangian scheme to deal with the convection terms, the finite element discretization to manage irregular geometries, a direct conjugate-gradient algorithm to solve the generalized Stokes problem, and an adaptive L-2-projection using quadrature rules to improve the efficiency and accuracy of the proposed method. In this study, the gradient of the temperature is used as an error indicator for the adaptation of enrichments by increasing the number of quadrature points where it is needed without refining the computational mesh. Unlike other adaptive finite element methods for coupled flow-transport problems, linear systems in the proposed enriched semiLagrangian finite element method preserve the same structure and size at each refinement in the adaptation procedure. In addition, due to the Lagrangian treatment of convection terms in this approach, the standard Courant-Friedrichs-Lewy condition is relaxed and the time truncation errors are reduced in the diffusion- reaction part. We assess the performance of the proposed method for a convection-diffusion problem with a known analytical solution and for the benchmark problem of thermal flow past a circular cylinder. We also solve a heat transport problem in the Mediterranean Sea to illustrate the ability of the method to resolve complex flow features in irregular geometries. Comparisons to the conventional semi-Lagrangian finite element method are also carried out in the current work. The obtained numerical results demonstrate the potential of the proposed method to capture the main flow features and support our expectations for an accurate and highly efficient enriched semi-Lagrangian finite element method for coupled flow-transport problems.

关键词： Enriched finite elements Semi-Lagrangian method Transport problems Incompressible Navier-Stokes equations L(2)projection adaptive algorithm

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On the effect of probing noise in optimal control LQR via Q-learning using adaptive filtering algorithms

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EUROPEAN JOURNAL OF CONTROL 2022年 65卷

作者： Lopez Yanez, Williams Jesus de Souza, Francisco das Chagas Fed Univ Maranhao UFMA Dept Elect Engn Adapt Syst & Signal Proc Lab LSAPS BR-65080805 Sao Luis Maranhao Brazil

The probing noise is essential to obtain the persistence of excitation condition for solving Bellman equations in optimal control problems based on reinforcement learning. However, the level of probing noise affects the system state and the input control signal. The Bellman equation solution can be obtained using the classical adaptive filtering algorithms, namely, normalized least-mean-square (NLMS) and recursive-least-squares (RLS);thus, taking into account this kind of solution, in this paper, we present an analysis on the effect of probing noise in the state and input of the dynamic system, considering the linear quadratic regulator (LQR) problem using Q-learning policy iteration. In our analysis, a closed formula for the autocovariance matrices of the system state and the input are obtained, showing that their norms are proportional to the variance of the probing noise during the learning process. Numerical experiments show the performance of Q-learning policy iteration method based on NLMS and RLS for different variances of probing noise. (c) 2022 European Control Association. Published by Elsevier Ltd. All rights reserved.

关键词： Optimal control Linear quadratic regulator Q-learning Probing noise adaptive algorithm

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